Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations268680
Missing cells565616
Missing cells (%)9.6%
Duplicate rows7256
Duplicate rows (%)2.7%
Total size in memory47.1 MiB
Average record size in memory184.0 B

Variable types

DateTime1
Categorical11
Numeric4
Text5
Unsupported1

Alerts

cantidad has constant value "1.0" Constant
fecha_ingestion has constant value "2024-10-06T04:07:03.000-05:00" Constant
Dataset has 7256 (2.7%) duplicate rowsDuplicates
arma_medio is highly overall correlated with modalidadHigh correlation
color is highly overall correlated with longitudHigh correlation
conducta_especial is highly overall correlated with longitudHigh correlation
longitud is highly overall correlated with color and 1 other fieldsHigh correlation
modalidad is highly overall correlated with arma_medioHigh correlation
medio_transporte is highly imbalanced (58.7%) Imbalance
modalidad is highly imbalanced (51.6%) Imbalance
conducta_especial is highly imbalanced (74.5%) Imbalance
grupo_bien is highly imbalanced (92.2%) Imbalance
latitud has 27713 (10.3%) missing values Missing
longitud has 27713 (10.3%) missing values Missing
estado_civil has 30215 (11.2%) missing values Missing
modalidad has 3206 (1.2%) missing values Missing
conducta_especial has 189000 (70.3%) missing values Missing
nombre_barrio has 4273 (1.6%) missing values Missing
lugar has 3390 (1.3%) missing values Missing
bien has 28481 (10.6%) missing values Missing
categoria_bien has 28481 (10.6%) missing values Missing
grupo_bien has 28481 (10.6%) missing values Missing
color has 192908 (71.8%) missing values Missing
longitud is highly skewed (γ1 = 159.3258111) Skewed
modelo is highly skewed (γ1 = 61.93217307) Skewed
codigo_comuna is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-03-10 15:56:20.237032
Analysis finished2025-03-10 15:56:47.962107
Duration27.73 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

Distinct120128
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Minimum2015-01-01 01:42:00-05:00
Maximum2023-11-30 23:30:00-05:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-10T15:56:48.085813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:48.514005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cantidad
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
1.0
268680 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters806040
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 268680
100.0%

Length

2025-03-10T15:56:48.701916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:56:48.796883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 268680
100.0%

Most occurring characters

ValueCountFrequency (%)
1 268680
33.3%
. 268680
33.3%
0 268680
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 806040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 268680
33.3%
. 268680
33.3%
0 268680
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 806040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 268680
33.3%
. 268680
33.3%
0 268680
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 806040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 268680
33.3%
. 268680
33.3%
0 268680
33.3%

latitud
Real number (ℝ)

Missing 

Distinct138969
Distinct (%)57.7%
Missing27713
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean6.2469163
Minimum3.8558707
Maximum10.155289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2025-03-10T15:56:48.970433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.8558707
5-th percentile6.2033975
Q16.2341018
median6.2489332
Q36.2599445
95-th percentile6.290568
Maximum10.155289
Range6.2994184
Interquartile range (IQR)0.025842725

Descriptive statistics

Standard deviation0.039385354
Coefficient of variation (CV)0.0063047673
Kurtosis2047.9278
Mean6.2469163
Median Absolute Deviation (MAD)0.0129595
Skewness-2.0511215
Sum1505300.7
Variance0.0015512061
MonotonicityNot monotonic
2025-03-10T15:56:49.407133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.24589 182
 
0.1%
6.249517 99
 
< 0.1%
6.209219 93
 
< 0.1%
6.244188 71
 
< 0.1%
6.24961 60
 
< 0.1%
6.24853 58
 
< 0.1%
6.26196335 51
 
< 0.1%
6.24801 39
 
< 0.1%
6.24805266 38
 
< 0.1%
6.276014 38
 
< 0.1%
Other values (138959) 240238
89.4%
(Missing) 27713
 
10.3%
ValueCountFrequency (%)
3.85587066 1
 
< 0.1%
4.18464903 1
 
< 0.1%
4.59446289 1
 
< 0.1%
4.5988 1
 
< 0.1%
4.60090219 3
< 0.1%
4.60513772 1
 
< 0.1%
4.60633519 1
 
< 0.1%
4.60805076 1
 
< 0.1%
4.61036634 1
 
< 0.1%
4.61440922 1
 
< 0.1%
ValueCountFrequency (%)
10.15528901 2
< 0.1%
9.76574341 2
< 0.1%
8.31189762 1
< 0.1%
7.17976381 1
< 0.1%
7.04894292 1
< 0.1%
6.85264251 1
< 0.1%
6.83082632 1
< 0.1%
6.50346583 2
< 0.1%
6.43796779 1
< 0.1%
6.41613322 2
< 0.1%

longitud
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct133715
Distinct (%)55.5%
Missing27713
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean-75.571143
Minimum-76.317445
Maximum75.60718
Zeros0
Zeros (%)0.0%
Negative240965
Negative (%)89.7%
Memory size4.1 MiB
2025-03-10T15:56:49.774775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-76.317445
5-th percentile-75.60598
Q1-75.587848
median-75.571928
Q3-75.565907
95-th percentile-75.552344
Maximum75.60718
Range151.92463
Interquartile range (IQR)0.021941395

Descriptive statistics

Standard deviation0.67214916
Coefficient of variation (CV)-0.0088942569
Kurtosis28532.789
Mean-75.571143
Median Absolute Deviation (MAD)0.00933954
Skewness159.32581
Sum-18210152
Variance0.45178449
MonotonicityNot monotonic
2025-03-10T15:56:50.110279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-75.57457 191
 
0.1%
-75.566524 99
 
< 0.1%
-75.567903 95
 
< 0.1%
-75.573663 71
 
< 0.1%
-75.5683 60
 
< 0.1%
-75.56543 58
 
< 0.1%
-75.56474 55
 
< 0.1%
-75.56636 53
 
< 0.1%
-75.56620138 53
 
< 0.1%
-75.56796104 39
 
< 0.1%
Other values (133705) 240193
89.4%
(Missing) 27713
 
10.3%
ValueCountFrequency (%)
-76.31744531 2
< 0.1%
-76.07574609 1
 
< 0.1%
-75.94391016 1
 
< 0.1%
-75.74615625 1
 
< 0.1%
-75.72418359 1
 
< 0.1%
-75.71146237 3
< 0.1%
-75.70939077 2
< 0.1%
-75.70741962 1
 
< 0.1%
-75.70658296 2
< 0.1%
-75.70569854 1
 
< 0.1%
ValueCountFrequency (%)
75.60718 2
 
< 0.1%
-0.000679 11
< 0.1%
-69.08844141 1
 
< 0.1%
-72.03277734 1
 
< 0.1%
-72.31842188 1
 
< 0.1%
-72.86773828 1
 
< 0.1%
-72.95562891 1
 
< 0.1%
-73.08746484 1
 
< 0.1%
-73.13141016 1
 
< 0.1%
-73.48297266 1
 
< 0.1%

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing37
Missing (%)< 0.1%
Memory size4.1 MiB
Hombre
155663 
Mujer
112980 

Length

Max length6
Median length6
Mean length5.5794419
Min length5

Characters and Unicode

Total characters1498878
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMujer
2nd rowMujer
3rd rowHombre
4th rowHombre
5th rowHombre

Common Values

ValueCountFrequency (%)
Hombre 155663
57.9%
Mujer 112980
42.1%
(Missing) 37
 
< 0.1%

Length

2025-03-10T15:56:50.647313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:56:50.937872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hombre 155663
57.9%
mujer 112980
42.1%

Most occurring characters

ValueCountFrequency (%)
r 268643
17.9%
e 268643
17.9%
H 155663
10.4%
o 155663
10.4%
m 155663
10.4%
b 155663
10.4%
M 112980
7.5%
u 112980
7.5%
j 112980
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1498878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 268643
17.9%
e 268643
17.9%
H 155663
10.4%
o 155663
10.4%
m 155663
10.4%
b 155663
10.4%
M 112980
7.5%
u 112980
7.5%
j 112980
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1498878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 268643
17.9%
e 268643
17.9%
H 155663
10.4%
o 155663
10.4%
m 155663
10.4%
b 155663
10.4%
M 112980
7.5%
u 112980
7.5%
j 112980
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1498878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 268643
17.9%
e 268643
17.9%
H 155663
10.4%
o 155663
10.4%
m 155663
10.4%
b 155663
10.4%
M 112980
7.5%
u 112980
7.5%
j 112980
7.5%

edad
Real number (ℝ)

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.351634
Minimum-1
Maximum121
Zeros0
Zeros (%)0.0%
Negative2932
Negative (%)1.1%
Memory size4.1 MiB
2025-03-10T15:56:51.184544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile19
Q125
median32
Q341
95-th percentile59
Maximum121
Range122
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.945927
Coefficient of variation (CV)0.37686496
Kurtosis0.8720687
Mean34.351634
Median Absolute Deviation (MAD)8
Skewness0.7161536
Sum9229597
Variance167.59703
MonotonicityNot monotonic
2025-03-10T15:56:51.495524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 10900
 
4.1%
27 10549
 
3.9%
25 10179
 
3.8%
26 10087
 
3.8%
28 10006
 
3.7%
30 9699
 
3.6%
29 9581
 
3.6%
24 9580
 
3.6%
32 9028
 
3.4%
31 8977
 
3.3%
Other values (88) 170094
63.3%
ValueCountFrequency (%)
-1 2932
1.1%
1 10
 
< 0.1%
2 23
 
< 0.1%
3 11
 
< 0.1%
4 10
 
< 0.1%
5 5
 
< 0.1%
6 7
 
< 0.1%
7 10
 
< 0.1%
8 9
 
< 0.1%
9 14
 
< 0.1%
ValueCountFrequency (%)
121 3
 
< 0.1%
96 7
 
< 0.1%
95 1
 
< 0.1%
94 3
 
< 0.1%
93 3
 
< 0.1%
92 7
 
< 0.1%
91 10
 
< 0.1%
90 13
< 0.1%
89 17
< 0.1%
88 28
< 0.1%

estado_civil
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing30215
Missing (%)11.2%
Memory size4.1 MiB
Soltero(a)
147626 
Casado(a)
43324 
Unión marital de hecho
38307 
Divorciado(a)
 
6944
Viudo(a)
 
2264

Length

Max length22
Median length10
Mean length11.814371
Min length8

Characters and Unicode

Total characters2817314
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnión marital de hecho
2nd rowSoltero(a)
3rd rowSoltero(a)
4th rowCasado(a)
5th rowCasado(a)

Common Values

ValueCountFrequency (%)
Soltero(a) 147626
54.9%
Casado(a) 43324
 
16.1%
Unión marital de hecho 38307
 
14.3%
Divorciado(a) 6944
 
2.6%
Viudo(a) 2264
 
0.8%
(Missing) 30215
 
11.2%

Length

2025-03-10T15:56:51.843491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:56:52.044182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
soltero(a 147626
41.8%
casado(a 43324
 
12.3%
unión 38307
 
10.8%
marital 38307
 
10.8%
de 38307
 
10.8%
hecho 38307
 
10.8%
divorciado(a 6944
 
2.0%
viudo(a 2264
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 393035
14.0%
a 370364
13.1%
e 224240
 
8.0%
) 200158
 
7.1%
( 200158
 
7.1%
r 192877
 
6.8%
l 185933
 
6.6%
t 185933
 
6.6%
S 147626
 
5.2%
114921
 
4.1%
Other values (14) 602069
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2817314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 393035
14.0%
a 370364
13.1%
e 224240
 
8.0%
) 200158
 
7.1%
( 200158
 
7.1%
r 192877
 
6.8%
l 185933
 
6.6%
t 185933
 
6.6%
S 147626
 
5.2%
114921
 
4.1%
Other values (14) 602069
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2817314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 393035
14.0%
a 370364
13.1%
e 224240
 
8.0%
) 200158
 
7.1%
( 200158
 
7.1%
r 192877
 
6.8%
l 185933
 
6.6%
t 185933
 
6.6%
S 147626
 
5.2%
114921
 
4.1%
Other values (14) 602069
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2817314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 393035
14.0%
a 370364
13.1%
e 224240
 
8.0%
) 200158
 
7.1%
( 200158
 
7.1%
r 192877
 
6.8%
l 185933
 
6.6%
t 185933
 
6.6%
S 147626
 
5.2%
114921
 
4.1%
Other values (14) 602069
21.4%

medio_transporte
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing38
Missing (%)< 0.1%
Memory size4.1 MiB
Caminata
208535 
Automóvil
22356 
Autobus
 
9995
Motocicleta
 
9526
Metro
 
7711
Other values (4)
 
10519

Length

Max length26
Median length8
Mean length8.0493854
Min length4

Characters and Unicode

Total characters2162403
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaminata
2nd rowCaminata
3rd rowCaminata
4th rowTaxi
5th rowCaminata

Common Values

ValueCountFrequency (%)
Caminata 208535
77.6%
Automóvil 22356
 
8.3%
Autobus 9995
 
3.7%
Motocicleta 9526
 
3.5%
Metro 7711
 
2.9%
Taxi 6806
 
2.5%
Bicicleta 2585
 
1.0%
Motocicleta con parrillero 1116
 
0.4%
Planeador 12
 
< 0.1%
(Missing) 38
 
< 0.1%

Length

2025-03-10T15:56:52.419507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:56:52.664423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
caminata 208535
77.0%
automóvil 22356
 
8.3%
motocicleta 10642
 
3.9%
autobus 9995
 
3.7%
metro 7711
 
2.8%
taxi 6806
 
2.5%
bicicleta 2585
 
1.0%
con 1116
 
0.4%
parrillero 1116
 
0.4%
planeador 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 646778
29.9%
t 272466
12.6%
i 254625
 
11.8%
m 230891
 
10.7%
n 209663
 
9.7%
C 208535
 
9.6%
o 63590
 
2.9%
u 42346
 
2.0%
l 37827
 
1.7%
A 32351
 
1.5%
Other values (15) 163331
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2162403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 646778
29.9%
t 272466
12.6%
i 254625
 
11.8%
m 230891
 
10.7%
n 209663
 
9.7%
C 208535
 
9.6%
o 63590
 
2.9%
u 42346
 
2.0%
l 37827
 
1.7%
A 32351
 
1.5%
Other values (15) 163331
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2162403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 646778
29.9%
t 272466
12.6%
i 254625
 
11.8%
m 230891
 
10.7%
n 209663
 
9.7%
C 208535
 
9.6%
o 63590
 
2.9%
u 42346
 
2.0%
l 37827
 
1.7%
A 32351
 
1.5%
Other values (15) 163331
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2162403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 646778
29.9%
t 272466
12.6%
i 254625
 
11.8%
m 230891
 
10.7%
n 209663
 
9.7%
C 208535
 
9.6%
o 63590
 
2.9%
u 42346
 
2.0%
l 37827
 
1.7%
A 32351
 
1.5%
Other values (15) 163331
 
7.6%

modalidad
Categorical

High correlation  Imbalance  Missing 

Distinct22
Distinct (%)< 0.1%
Missing3206
Missing (%)1.2%
Memory size4.1 MiB
Atraco
135172 
Descuido
53214 
Cosquilleo
37827 
Raponazo
17418 
Engaño
 
5056
Other values (17)
16787 

Length

Max length22
Median length6
Mean length7.7880922
Min length6

Characters and Unicode

Total characters2067536
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAtraco
2nd rowAtraco
3rd rowDescuido
4th rowAtraco
5th rowEngaño

Common Values

ValueCountFrequency (%)
Atraco 135172
50.3%
Descuido 53214
 
19.8%
Cosquilleo 37827
 
14.1%
Raponazo 17418
 
6.5%
Engaño 5056
 
1.9%
Rompimiento cerraduta 4281
 
1.6%
Escopolamina 3776
 
1.4%
Rompimiento de ventana 3272
 
1.2%
Halado 1748
 
0.7%
Rompimiento cerradura 1715
 
0.6%
Other values (12) 1995
 
0.7%
(Missing) 3206
 
1.2%

Length

2025-03-10T15:56:53.109571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atraco 135172
48.3%
descuido 53214
 
19.0%
cosquilleo 37827
 
13.5%
raponazo 17418
 
6.2%
rompimiento 9268
 
3.3%
engaño 5056
 
1.8%
cerraduta 4281
 
1.5%
de 4093
 
1.5%
escopolamina 3776
 
1.3%
ventana 3272
 
1.2%
Other values (20) 6765
 
2.4%

Most occurring characters

ValueCountFrequency (%)
o 333942
16.2%
a 206568
10.0%
c 199082
9.6%
t 154021
 
7.4%
r 149125
 
7.2%
A 135172
 
6.5%
e 116467
 
5.6%
i 116447
 
5.6%
u 97801
 
4.7%
s 95709
 
4.6%
Other values (27) 463202
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2067536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 333942
16.2%
a 206568
10.0%
c 199082
9.6%
t 154021
 
7.4%
r 149125
 
7.2%
A 135172
 
6.5%
e 116467
 
5.6%
i 116447
 
5.6%
u 97801
 
4.7%
s 95709
 
4.6%
Other values (27) 463202
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2067536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 333942
16.2%
a 206568
10.0%
c 199082
9.6%
t 154021
 
7.4%
r 149125
 
7.2%
A 135172
 
6.5%
e 116467
 
5.6%
i 116447
 
5.6%
u 97801
 
4.7%
s 95709
 
4.6%
Other values (27) 463202
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2067536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 333942
16.2%
a 206568
10.0%
c 199082
9.6%
t 154021
 
7.4%
r 149125
 
7.2%
A 135172
 
6.5%
e 116467
 
5.6%
i 116447
 
5.6%
u 97801
 
4.7%
s 95709
 
4.6%
Other values (27) 463202
22.4%

conducta_especial
Categorical

High correlation  Imbalance  Missing 

Distinct29
Distinct (%)< 0.1%
Missing189000
Missing (%)70.3%
Memory size4.1 MiB
No
48403 
De celular
28195 
Fleteo
 
1398
A vehículo repartidor
 
776
A taxista
 
395
Other values (24)
 
513

Length

Max length25
Median length2
Mean length5.2298318
Min length2

Characters and Unicode

Total characters416713
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDe celular
2nd rowDe celular
3rd rowDe celular
4th rowNo
5th rowDe celular

Common Values

ValueCountFrequency (%)
No 48403
 
18.0%
De celular 28195
 
10.5%
Fleteo 1398
 
0.5%
A vehículo repartidor 776
 
0.3%
A taxista 395
 
0.1%
A bus de servicio público 226
 
0.1%
Violencia contra la mujer 52
 
< 0.1%
Secuestro 47
 
< 0.1%
De cable 29
 
< 0.1%
Homicidio 22
 
< 0.1%
Other values (19) 137
 
0.1%
(Missing) 189000
70.3%

Length

2025-03-10T15:56:53.499282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 48403
43.6%
de 28475
25.6%
celular 28195
25.4%
fleteo 1398
 
1.3%
a 1397
 
1.3%
vehículo 781
 
0.7%
repartidor 776
 
0.7%
taxista 395
 
0.4%
servicio 228
 
0.2%
público 228
 
0.2%
Other values (35) 746
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 61592
14.8%
l 59006
14.2%
o 52180
12.5%
N 48403
11.6%
31342
7.5%
r 31028
7.4%
a 30130
7.2%
c 29708
7.1%
u 29344
7.0%
D 28240
6.8%
Other values (30) 15740
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 416713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 61592
14.8%
l 59006
14.2%
o 52180
12.5%
N 48403
11.6%
31342
7.5%
r 31028
7.4%
a 30130
7.2%
c 29708
7.1%
u 29344
7.0%
D 28240
6.8%
Other values (30) 15740
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 416713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 61592
14.8%
l 59006
14.2%
o 52180
12.5%
N 48403
11.6%
31342
7.5%
r 31028
7.4%
a 30130
7.2%
c 29708
7.1%
u 29344
7.0%
D 28240
6.8%
Other values (30) 15740
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 416713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 61592
14.8%
l 59006
14.2%
o 52180
12.5%
N 48403
11.6%
31342
7.5%
r 31028
7.4%
a 30130
7.2%
c 29708
7.1%
u 29344
7.0%
D 28240
6.8%
Other values (30) 15740
 
3.8%

arma_medio
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing1680
Missing (%)0.6%
Memory size4.1 MiB
No
133925 
Arma de fuego
67462 
Arma cortopunzante
48509 
Objeto contundente
 
11274
Escopolamina
 
5774
Other values (4)
 
56

Length

Max length18
Median length2
Mean length8.5800787
Min length2

Characters and Unicode

Total characters2290881
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowArma cortopunzante
2nd rowArma cortopunzante
3rd rowNo
4th rowArma de fuego
5th rowNo

Common Values

ValueCountFrequency (%)
No 133925
49.8%
Arma de fuego 67462
25.1%
Arma cortopunzante 48509
 
18.1%
Objeto contundente 11274
 
4.2%
Escopolamina 5774
 
2.1%
Llave maestra 41
 
< 0.1%
Perro 11
 
< 0.1%
Tóxico o Químico 3
 
< 0.1%
Palanca 1
 
< 0.1%
(Missing) 1680
 
0.6%

Length

2025-03-10T15:56:53.933946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:56:54.254387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 133925
29.0%
arma 115971
25.1%
de 67462
14.6%
fuego 67462
14.6%
cortopunzante 48509
 
10.5%
objeto 11274
 
2.4%
contundente 11274
 
2.4%
escopolamina 5774
 
1.3%
llave 41
 
< 0.1%
maestra 41
 
< 0.1%
Other values (5) 21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 332521
14.5%
e 217348
 
9.5%
194754
 
8.5%
a 176154
 
7.7%
r 164543
 
7.2%
n 136615
 
6.0%
N 133925
 
5.8%
t 130881
 
5.7%
u 127248
 
5.6%
m 121789
 
5.3%
Other values (22) 555103
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2290881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 332521
14.5%
e 217348
 
9.5%
194754
 
8.5%
a 176154
 
7.7%
r 164543
 
7.2%
n 136615
 
6.0%
N 133925
 
5.8%
t 130881
 
5.7%
u 127248
 
5.6%
m 121789
 
5.3%
Other values (22) 555103
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2290881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 332521
14.5%
e 217348
 
9.5%
194754
 
8.5%
a 176154
 
7.7%
r 164543
 
7.2%
n 136615
 
6.0%
N 133925
 
5.8%
t 130881
 
5.7%
u 127248
 
5.6%
m 121789
 
5.3%
Other values (22) 555103
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2290881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 332521
14.5%
e 217348
 
9.5%
194754
 
8.5%
a 176154
 
7.7%
r 164543
 
7.2%
n 136615
 
6.0%
N 133925
 
5.8%
t 130881
 
5.7%
u 127248
 
5.6%
m 121789
 
5.3%
Other values (22) 555103
24.2%

nombre_barrio
Text

Missing 

Distinct343
Distinct (%)0.1%
Missing4273
Missing (%)1.6%
Memory size4.1 MiB
2025-03-10T15:56:55.367866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length29
Mean length11.858135
Min length4

Characters and Unicode

Total characters3135374
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowDoce de Octubre No.2
2nd rowLa Candelaria
3rd rowCampo Valdés No.1
4th rowCampo Valdés No.1
5th rowNaranjal
ValueCountFrequency (%)
la 50362
 
10.0%
candelaria 30973
 
6.1%
san 18997
 
3.8%
el 18403
 
3.6%
de 13545
 
2.7%
los 12350
 
2.4%
villa 11282
 
2.2%
barrio 9538
 
1.9%
nueva 9095
 
1.8%
poblado 7956
 
1.6%
Other values (375) 322422
63.9%
2025-03-10T15:56:57.398345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 504225
16.1%
240517
 
7.7%
o 237343
 
7.6%
r 213478
 
6.8%
l 209025
 
6.7%
e 197153
 
6.3%
i 179305
 
5.7%
n 176611
 
5.6%
s 117340
 
3.7%
d 103012
 
3.3%
Other values (59) 957365
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3135374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 504225
16.1%
240517
 
7.7%
o 237343
 
7.6%
r 213478
 
6.8%
l 209025
 
6.7%
e 197153
 
6.3%
i 179305
 
5.7%
n 176611
 
5.6%
s 117340
 
3.7%
d 103012
 
3.3%
Other values (59) 957365
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3135374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 504225
16.1%
240517
 
7.7%
o 237343
 
7.6%
r 213478
 
6.8%
l 209025
 
6.7%
e 197153
 
6.3%
i 179305
 
5.7%
n 176611
 
5.6%
s 117340
 
3.7%
d 103012
 
3.3%
Other values (59) 957365
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3135374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 504225
16.1%
240517
 
7.7%
o 237343
 
7.6%
r 213478
 
6.8%
l 209025
 
6.7%
e 197153
 
6.3%
i 179305
 
5.7%
n 176611
 
5.6%
s 117340
 
3.7%
d 103012
 
3.3%
Other values (59) 957365
30.5%
Distinct371
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2025-03-10T15:56:58.577813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length5
Mean length5.1818967
Min length3

Characters and Unicode

Total characters1392272
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row#0603
2nd row#1019
3rd row#0410
4th row#0410
5th row#1103
ValueCountFrequency (%)
1019 30968
 
11.3%
1418 7594
 
2.8%
1007 6108
 
2.2%
1013 5644
 
2.1%
1001 4739
 
1.7%
sin 4607
 
1.7%
dato 4607
 
1.7%
1006 4554
 
1.7%
1018 4290
 
1.6%
1108 4283
 
1.6%
Other values (358) 196981
71.8%
2025-03-10T15:56:59.145593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 372435
26.8%
0 274531
19.7%
# 257186
18.5%
4 65047
 
4.7%
9 57211
 
4.1%
2 48217
 
3.5%
7 41577
 
3.0%
6 41454
 
3.0%
3 40997
 
2.9%
5 40785
 
2.9%
Other values (27) 152832
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1392272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 372435
26.8%
0 274531
19.7%
# 257186
18.5%
4 65047
 
4.7%
9 57211
 
4.1%
2 48217
 
3.5%
7 41577
 
3.0%
6 41454
 
3.0%
3 40997
 
2.9%
5 40785
 
2.9%
Other values (27) 152832
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1392272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 372435
26.8%
0 274531
19.7%
# 257186
18.5%
4 65047
 
4.7%
9 57211
 
4.1%
2 48217
 
3.5%
7 41577
 
3.0%
6 41454
 
3.0%
3 40997
 
2.9%
5 40785
 
2.9%
Other values (27) 152832
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1392272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 372435
26.8%
0 274531
19.7%
# 257186
18.5%
4 65047
 
4.7%
9 57211
 
4.1%
2 48217
 
3.5%
7 41577
 
3.0%
6 41454
 
3.0%
3 40997
 
2.9%
5 40785
 
2.9%
Other values (27) 152832
11.0%

codigo_comuna
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.1 MiB

lugar
Text

Missing 

Distinct90
Distinct (%)< 0.1%
Missing3390
Missing (%)1.3%
Memory size4.1 MiB
2025-03-10T15:56:59.467464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length53
Median length11
Mean length13.849531
Min length4

Characters and Unicode

Total characters3674142
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowVía pública
2nd rowVía pública
3rd rowVía pública
4th rowVía pública
5th rowVía pública
ValueCountFrequency (%)
pública 137683
22.7%
vía 137556
22.7%
de 18616
 
3.1%
y 18396
 
3.0%
metro 17858
 
2.9%
o 16952
 
2.8%
estación 16383
 
2.7%
del 15574
 
2.6%
otro 11582
 
1.9%
tienda 11582
 
1.9%
Other values (133) 204358
33.7%
2025-03-10T15:57:00.006295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 470435
 
12.8%
341250
 
9.3%
i 290496
 
7.9%
c 260511
 
7.1%
l 234333
 
6.4%
e 196858
 
5.4%
o 172310
 
4.7%
p 171324
 
4.7%
b 154285
 
4.2%
í 147817
 
4.0%
Other values (43) 1234523
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3674142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 470435
 
12.8%
341250
 
9.3%
i 290496
 
7.9%
c 260511
 
7.1%
l 234333
 
6.4%
e 196858
 
5.4%
o 172310
 
4.7%
p 171324
 
4.7%
b 154285
 
4.2%
í 147817
 
4.0%
Other values (43) 1234523
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3674142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 470435
 
12.8%
341250
 
9.3%
i 290496
 
7.9%
c 260511
 
7.1%
l 234333
 
6.4%
e 196858
 
5.4%
o 172310
 
4.7%
p 171324
 
4.7%
b 154285
 
4.2%
í 147817
 
4.0%
Other values (43) 1234523
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3674142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 470435
 
12.8%
341250
 
9.3%
i 290496
 
7.9%
c 260511
 
7.1%
l 234333
 
6.4%
e 196858
 
5.4%
o 172310
 
4.7%
p 171324
 
4.7%
b 154285
 
4.2%
í 147817
 
4.0%
Other values (43) 1234523
33.6%

sede_receptora
Categorical

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
Candelaria
81316 
Laureles
49712 
Belén
31704 
Poblado
30818 
Castilla
25747 
Other values (19)
49383 

Length

Max length24
Median length20
Mean length8.6406766
Min length5

Characters and Unicode

Total characters2321577
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDoce de Octubre
2nd rowCandelaria
3rd rowAranjuez
4th rowAranjuez
5th rowLaureles

Common Values

ValueCountFrequency (%)
Candelaria 81316
30.3%
Laureles 49712
18.5%
Belén 31704
 
11.8%
Poblado 30818
 
11.5%
Castilla 25747
 
9.6%
Aranjuez 14102
 
5.2%
Buenos Aires 9797
 
3.6%
Villa Hermosa 5664
 
2.1%
Doce de Octubre 5649
 
2.1%
Manrique 4243
 
1.6%
Other values (14) 9928
 
3.7%

Length

2025-03-10T15:57:00.414682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
candelaria 81316
26.6%
laureles 49712
16.2%
belén 31704
 
10.4%
poblado 30818
 
10.1%
castilla 25747
 
8.4%
aranjuez 14102
 
4.6%
buenos 9797
 
3.2%
aires 9797
 
3.2%
de 7173
 
2.3%
san 5732
 
1.9%
Other values (26) 40278
13.2%

Most occurring characters

ValueCountFrequency (%)
a 423232
18.2%
e 278842
12.0%
l 258658
11.1%
r 180353
 
7.8%
n 151895
 
6.5%
i 132508
 
5.7%
d 120841
 
5.2%
C 108999
 
4.7%
s 100721
 
4.3%
o 89557
 
3.9%
Other values (31) 475971
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2321577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 423232
18.2%
e 278842
12.0%
l 258658
11.1%
r 180353
 
7.8%
n 151895
 
6.5%
i 132508
 
5.7%
d 120841
 
5.2%
C 108999
 
4.7%
s 100721
 
4.3%
o 89557
 
3.9%
Other values (31) 475971
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2321577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 423232
18.2%
e 278842
12.0%
l 258658
11.1%
r 180353
 
7.8%
n 151895
 
6.5%
i 132508
 
5.7%
d 120841
 
5.2%
C 108999
 
4.7%
s 100721
 
4.3%
o 89557
 
3.9%
Other values (31) 475971
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2321577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 423232
18.2%
e 278842
12.0%
l 258658
11.1%
r 180353
 
7.8%
n 151895
 
6.5%
i 132508
 
5.7%
d 120841
 
5.2%
C 108999
 
4.7%
s 100721
 
4.3%
o 89557
 
3.9%
Other values (31) 475971
20.5%

bien
Text

Missing 

Distinct415
Distinct (%)0.2%
Missing28481
Missing (%)10.6%
Memory size4.1 MiB
2025-03-10T15:57:00.729260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length41
Mean length10.001474
Min length2

Characters and Unicode

Total characters2402344
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)< 0.1%

Sample

1st rowCelular
2nd rowCelular
3rd rowCelular
4th rowCelular
5th rowBilletera
ValueCountFrequency (%)
celular 85249
24.4%
peso 42467
12.2%
accesorios 20436
 
5.9%
de 18572
 
5.3%
prendas 16205
 
4.6%
vestir 16074
 
4.6%
cédula 15665
 
4.5%
computador 12650
 
3.6%
tarjeta 11428
 
3.3%
bancaria 9632
 
2.8%
Other values (490) 100529
28.8%
2025-03-10T15:57:01.226883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 291187
12.1%
a 252072
 
10.5%
l 231271
 
9.6%
r 210091
 
8.7%
o 174257
 
7.3%
s 148443
 
6.2%
u 127310
 
5.3%
C 113280
 
4.7%
108708
 
4.5%
i 106572
 
4.4%
Other values (50) 639153
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2402344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 291187
12.1%
a 252072
 
10.5%
l 231271
 
9.6%
r 210091
 
8.7%
o 174257
 
7.3%
s 148443
 
6.2%
u 127310
 
5.3%
C 113280
 
4.7%
108708
 
4.5%
i 106572
 
4.4%
Other values (50) 639153
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2402344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 291187
12.1%
a 252072
 
10.5%
l 231271
 
9.6%
r 210091
 
8.7%
o 174257
 
7.3%
s 148443
 
6.2%
u 127310
 
5.3%
C 113280
 
4.7%
108708
 
4.5%
i 106572
 
4.4%
Other values (50) 639153
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2402344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 291187
12.1%
a 252072
 
10.5%
l 231271
 
9.6%
r 210091
 
8.7%
o 174257
 
7.3%
s 148443
 
6.2%
u 127310
 
5.3%
C 113280
 
4.7%
108708
 
4.5%
i 106572
 
4.4%
Other values (50) 639153
26.6%

categoria_bien
Text

Missing 

Distinct52
Distinct (%)< 0.1%
Missing28481
Missing (%)10.6%
Memory size4.1 MiB
2025-03-10T15:57:01.445441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length10
Mean length21.674724
Min length4

Characters and Unicode

Total characters5206247
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowTecnología
2nd rowTecnología
3rd rowTecnología
4th rowTecnología
5th rowPrendas de vestir y accesorios
ValueCountFrequency (%)
tecnología 102614
14.1%
y 85117
11.7%
valor 53930
 
7.4%
título 53930
 
7.4%
dinero 53930
 
7.4%
preciosas 53930
 
7.4%
piedras 53930
 
7.4%
joyas 53930
 
7.4%
de 33986
 
4.7%
documentos 28250
 
3.9%
Other values (82) 151907
20.9%
2025-03-10T15:57:01.862689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 639271
12.3%
485255
 
9.3%
e 470120
 
9.0%
a 404243
 
7.8%
s 391307
 
7.5%
r 328597
 
6.3%
c 260628
 
5.0%
i 240509
 
4.6%
l 239704
 
4.6%
n 232389
 
4.5%
Other values (41) 1514224
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5206247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 639271
12.3%
485255
 
9.3%
e 470120
 
9.0%
a 404243
 
7.8%
s 391307
 
7.5%
r 328597
 
6.3%
c 260628
 
5.0%
i 240509
 
4.6%
l 239704
 
4.6%
n 232389
 
4.5%
Other values (41) 1514224
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5206247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 639271
12.3%
485255
 
9.3%
e 470120
 
9.0%
a 404243
 
7.8%
s 391307
 
7.5%
r 328597
 
6.3%
c 260628
 
5.0%
i 240509
 
4.6%
l 239704
 
4.6%
n 232389
 
4.5%
Other values (41) 1514224
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5206247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 639271
12.3%
485255
 
9.3%
e 470120
 
9.0%
a 404243
 
7.8%
s 391307
 
7.5%
r 328597
 
6.3%
c 260628
 
5.0%
i 240509
 
4.6%
l 239704
 
4.6%
n 232389
 
4.5%
Other values (41) 1514224
29.1%

grupo_bien
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing28481
Missing (%)10.6%
Memory size4.1 MiB
Mercancía
234248 
Vehículo
 
5346
Bélico
 
440
Equipamiento
 
162
Estupefaciente
 
3

Length

Max length14
Median length9
Mean length8.9743338
Min length6

Characters and Unicode

Total characters2155626
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMercancía
2nd rowMercancía
3rd rowMercancía
4th rowMercancía
5th rowMercancía

Common Values

ValueCountFrequency (%)
Mercancía 234248
87.2%
Vehículo 5346
 
2.0%
Bélico 440
 
0.2%
Equipamiento 162
 
0.1%
Estupefaciente 3
 
< 0.1%
(Missing) 28481
 
10.6%

Length

2025-03-10T15:57:02.030072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:57:02.151980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mercancía 234248
97.5%
vehículo 5346
 
2.2%
bélico 440
 
0.2%
equipamiento 162
 
0.1%
estupefaciente 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 474285
22.0%
a 468661
21.7%
e 239765
11.1%
í 239594
11.1%
n 234413
10.9%
M 234248
10.9%
r 234248
10.9%
o 5948
 
0.3%
l 5786
 
0.3%
u 5511
 
0.3%
Other values (12) 13167
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2155626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 474285
22.0%
a 468661
21.7%
e 239765
11.1%
í 239594
11.1%
n 234413
10.9%
M 234248
10.9%
r 234248
10.9%
o 5948
 
0.3%
l 5786
 
0.3%
u 5511
 
0.3%
Other values (12) 13167
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2155626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 474285
22.0%
a 468661
21.7%
e 239765
11.1%
í 239594
11.1%
n 234413
10.9%
M 234248
10.9%
r 234248
10.9%
o 5948
 
0.3%
l 5786
 
0.3%
u 5511
 
0.3%
Other values (12) 13167
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2155626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 474285
22.0%
a 468661
21.7%
e 239765
11.1%
í 239594
11.1%
n 234413
10.9%
M 234248
10.9%
r 234248
10.9%
o 5948
 
0.3%
l 5786
 
0.3%
u 5511
 
0.3%
Other values (12) 13167
 
0.6%

modelo
Real number (ℝ)

Skewed 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.47459059
Minimum-1
Maximum2023
Zeros167
Zeros (%)0.1%
Negative268443
Negative (%)99.9%
Memory size4.1 MiB
2025-03-10T15:57:02.299417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum2023
Range2024
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.508868
Coefficient of variation (CV)-68.498761
Kurtosis3833.7133
Mean-0.47459059
Median Absolute Deviation (MAD)0
Skewness61.932173
Sum-127513
Variance1056.8265
MonotonicityNot monotonic
2025-03-10T15:57:02.461685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
-1 268443
99.9%
0 167
 
0.1%
2015 10
 
< 0.1%
2014 7
 
< 0.1%
2022 6
 
< 0.1%
2020 6
 
< 0.1%
2013 6
 
< 0.1%
2018 5
 
< 0.1%
2005 5
 
< 0.1%
2019 5
 
< 0.1%
Other values (12) 20
 
< 0.1%
ValueCountFrequency (%)
-1 268443
99.9%
0 167
 
0.1%
1950 1
 
< 0.1%
1998 2
 
< 0.1%
1999 1
 
< 0.1%
2000 2
 
< 0.1%
2005 5
 
< 0.1%
2008 2
 
< 0.1%
2009 2
 
< 0.1%
2011 2
 
< 0.1%
ValueCountFrequency (%)
2023 2
 
< 0.1%
2022 6
< 0.1%
2021 1
 
< 0.1%
2020 6
< 0.1%
2019 5
< 0.1%
2018 5
< 0.1%
2017 2
 
< 0.1%
2016 1
 
< 0.1%
2015 10
< 0.1%
2014 7
< 0.1%

color
Categorical

High correlation  Missing 

Distinct15
Distinct (%)< 0.1%
Missing192908
Missing (%)71.8%
Memory size4.1 MiB
Negro
35520 
Azul
11681 
Blanco
7763 
Oro
5853 
Gris
5599 
Other values (10)
9356 

Length

Max length8
Median length5
Mean length4.7667344
Min length3

Characters and Unicode

Total characters361185
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegro
2nd rowNegro
3rd rowNegro
4th rowNegro
5th rowNegro

Common Values

ValueCountFrequency (%)
Negro 35520
 
13.2%
Azul 11681
 
4.3%
Blanco 7763
 
2.9%
Oro 5853
 
2.2%
Gris 5599
 
2.1%
Plata 2338
 
0.9%
Verde 1325
 
0.5%
Rosado 1187
 
0.4%
Rojo 1157
 
0.4%
Morado 925
 
0.3%
Other values (5) 2424
 
0.9%
(Missing) 192908
71.8%

Length

2025-03-10T15:57:02.632694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
negro 35520
46.9%
azul 11681
 
15.4%
blanco 7763
 
10.2%
oro 5853
 
7.7%
gris 5599
 
7.4%
plata 2338
 
3.1%
verde 1325
 
1.7%
rosado 1187
 
1.6%
rojo 1157
 
1.5%
morado 925
 
1.2%
Other values (5) 2424
 
3.2%

Most occurring characters

ValueCountFrequency (%)
o 56798
15.7%
r 50572
14.0%
e 39091
10.8%
g 35857
9.9%
N 35746
9.9%
l 23536
 
6.5%
a 16843
 
4.7%
A 12558
 
3.5%
z 11681
 
3.2%
u 11681
 
3.2%
Other values (18) 66822
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 56798
15.7%
r 50572
14.0%
e 39091
10.8%
g 35857
9.9%
N 35746
9.9%
l 23536
 
6.5%
a 16843
 
4.7%
A 12558
 
3.5%
z 11681
 
3.2%
u 11681
 
3.2%
Other values (18) 66822
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 56798
15.7%
r 50572
14.0%
e 39091
10.8%
g 35857
9.9%
N 35746
9.9%
l 23536
 
6.5%
a 16843
 
4.7%
A 12558
 
3.5%
z 11681
 
3.2%
u 11681
 
3.2%
Other values (18) 66822
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 56798
15.7%
r 50572
14.0%
e 39091
10.8%
g 35857
9.9%
N 35746
9.9%
l 23536
 
6.5%
a 16843
 
4.7%
A 12558
 
3.5%
z 11681
 
3.2%
u 11681
 
3.2%
Other values (18) 66822
18.5%

fecha_ingestion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-10-06T04:07:03.000-05:00
268680 

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters7791720
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-10-06T04:07:03.000-05:00
2nd row2024-10-06T04:07:03.000-05:00
3rd row2024-10-06T04:07:03.000-05:00
4th row2024-10-06T04:07:03.000-05:00
5th row2024-10-06T04:07:03.000-05:00

Common Values

ValueCountFrequency (%)
2024-10-06T04:07:03.000-05:00 268680
100.0%

Length

2025-03-10T15:57:02.775722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T15:57:02.864672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2024-10-06t04:07:03.000-05:00 268680
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3224160
41.4%
- 806040
 
10.3%
: 806040
 
10.3%
2 537360
 
6.9%
4 537360
 
6.9%
1 268680
 
3.4%
6 268680
 
3.4%
T 268680
 
3.4%
7 268680
 
3.4%
3 268680
 
3.4%
Other values (2) 537360
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7791720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3224160
41.4%
- 806040
 
10.3%
: 806040
 
10.3%
2 537360
 
6.9%
4 537360
 
6.9%
1 268680
 
3.4%
6 268680
 
3.4%
T 268680
 
3.4%
7 268680
 
3.4%
3 268680
 
3.4%
Other values (2) 537360
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7791720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3224160
41.4%
- 806040
 
10.3%
: 806040
 
10.3%
2 537360
 
6.9%
4 537360
 
6.9%
1 268680
 
3.4%
6 268680
 
3.4%
T 268680
 
3.4%
7 268680
 
3.4%
3 268680
 
3.4%
Other values (2) 537360
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7791720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3224160
41.4%
- 806040
 
10.3%
: 806040
 
10.3%
2 537360
 
6.9%
4 537360
 
6.9%
1 268680
 
3.4%
6 268680
 
3.4%
T 268680
 
3.4%
7 268680
 
3.4%
3 268680
 
3.4%
Other values (2) 537360
 
6.9%

Interactions

2025-03-10T15:56:42.421097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:39.824650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:40.798121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:41.636043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:42.661824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:40.023444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:40.987270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:41.828542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:42.978297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:40.401887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:41.197613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:42.029342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:43.276365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:40.608433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:41.403208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-10T15:56:42.227780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-10T15:57:02.961998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
arma_mediocolorconducta_especialedadestado_civilgrupo_bienlatitudlongitudmedio_transportemodalidadmodelosede_receptorasexo
arma_medio1.0000.0350.1240.0430.0430.0360.0120.0000.1230.5050.0030.0810.168
color0.0351.0000.0580.0280.0220.0740.0001.0000.0410.0370.0220.0190.171
conducta_especial0.1240.0581.0000.0540.0500.0430.0631.0000.2050.1690.0000.0490.112
edad0.0430.0280.0541.0000.2600.012-0.013-0.0030.0430.065-0.0030.0380.038
estado_civil0.0430.0220.0500.2601.0000.0110.0000.0000.0680.0520.0070.0530.094
grupo_bien0.0360.0740.0430.0120.0111.0000.0240.0000.1790.2600.1130.0360.064
latitud0.0120.0000.063-0.0130.0000.0241.0000.1490.0000.0000.0090.0040.001
longitud0.0001.0001.000-0.0030.0000.0000.1491.0000.0000.0000.0010.0050.003
medio_transporte0.1230.0410.2050.0430.0680.1790.0000.0001.0000.1970.0120.0710.132
modalidad0.5050.0370.1690.0650.0520.2600.0000.0000.1971.0000.0120.0550.214
modelo0.0030.0220.000-0.0030.0070.1130.0090.0010.0120.0121.0000.0180.004
sede_receptora0.0810.0190.0490.0380.0530.0360.0040.0050.0710.0550.0181.0000.035
sexo0.1680.1710.1120.0380.0940.0640.0010.0030.1320.2140.0040.0351.000

Missing values

2025-03-10T15:56:44.118870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-10T15:56:45.531843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-10T15:56:47.169874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fecha_hechocantidadlatitudlongitudsexoedadestado_civilmedio_transportemodalidadconducta_especialarma_medionombre_barriocodigo_barriocodigo_comunalugarsede_receptorabiencategoria_biengrupo_bienmodelocolorfecha_ingestion
02017-01-01 01:00:00-05:001.06.299703-75.582016Mujer33Unión marital de hechoCaminataAtracoDe celularArma cortopunzanteDoce de Octubre No.2#06036Vía públicaDoce de OctubreCelularTecnologíaMercancía-1NaN2024-10-06T04:07:03.000-05:00
12017-01-01 15:00:00-05:001.06.250917-75.566160Mujer26Soltero(a)CaminataAtracoNaNArma cortopunzanteLa Candelaria#101910Vía públicaCandelariaCelularTecnologíaMercancía-1NaN2024-10-06T04:07:03.000-05:00
22017-01-01 14:00:00-05:001.06.274836-75.554909Hombre30Soltero(a)CaminataDescuidoDe celularNoCampo Valdés No.1#04104Vía públicaAranjuezCelularTecnologíaMercancía-1Negro2024-10-06T04:07:03.000-05:00
32017-01-01 16:00:00-05:001.06.276236-75.553533Hombre37Casado(a)TaxiAtracoDe celularArma de fuegoCampo Valdés No.1#04104Vía públicaAranjuezCelularTecnologíaMercancía-1Negro2024-10-06T04:07:03.000-05:00
42017-01-01 16:00:00-05:001.06.250339-75.586914Hombre87Casado(a)CaminataEngañoNaNNoNaranjal#110311Vía públicaLaurelesBilleteraPrendas de vestir y accesoriosMercancía-1NaN2024-10-06T04:07:03.000-05:00
52017-01-01 16:00:00-05:001.06.219071-75.602996Mujer29Unión marital de hechoTaxiDescuidoNoNoLa Loma de los Bernal#161116Vía públicaBelénElementos escolaresElementos escolaresMercancía-1NaN2024-10-06T04:07:03.000-05:00
62017-01-01 20:30:00-05:001.06.317709-75.678259Hombre27Soltero(a)MotocicletaAtracoNaNArma de fuegoVolcana Guayabal#500550Vía públicaDoce de OctubreCámaraTecnologíaMercancía-1NaN2024-10-06T04:07:03.000-05:00
72017-01-01 16:00:00-05:001.06.268307-75.558457Hombre36Soltero(a)CaminataDescuidoNaNNoManrique Central No.1#04094Hospital o centro de saludAranjuezPesoDinero, joyas, piedras preciosas y título valorMercancía-1NaN2024-10-06T04:07:03.000-05:00
82017-01-01 00:30:00-05:001.06.242797-75.554797Hombre41Casado(a)CaminataAtracoDe celularArma cortopunzanteBarrio Caycedo#09069Vía públicaBuenos AiresPesoDinero, joyas, piedras preciosas y título valorMercancía-1NaN2024-10-06T04:07:03.000-05:00
92017-01-01 08:00:00-05:001.06.246823-75.562114Hombre59Soltero(a)CaminataDescuidoNoNoBoston#101610Vía públicaCandelariaPesoDinero, joyas, piedras preciosas y título valorMercancía-1NaN2024-10-06T04:07:03.000-05:00
fecha_hechocantidadlatitudlongitudsexoedadestado_civilmedio_transportemodalidadconducta_especialarma_medionombre_barriocodigo_barriocodigo_comunalugarsede_receptorabiencategoria_biengrupo_bienmodelocolorfecha_ingestion
3264792023-11-30 19:00:00-05:001.0NaNNaNMujer25NaNCaminataAtracoNaNArma cortopunzanteCastilla#05115Centro comercialCastillaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264802023-11-30 20:00:00-05:001.0NaNNaNHombre20NaNCaminataAtracoNaNNaNNaNSIN DATOSIN DATOVía públicaLaurelesNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264812023-11-30 20:00:00-05:001.0NaNNaNHombre32NaNCaminataAtracoNaNArma cortopunzanteSan Diego#102010Paradero de busCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264822023-11-30 20:45:00-05:001.0NaNNaNHombre38NaNCaminataFleteoNaNNoNaNSIN DATOSIN DATOVía públicaCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264832023-11-30 20:58:00-05:001.0NaNNaNHombre59NaNCaminataAtracoNaNArma de fuegoNaNSIN DATOSIN DATOVía públicaCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264842023-11-30 21:30:00-05:001.0NaNNaNHombre29NaNCaminataCosquilleoNaNNoNaNSIN DATOSIN DATOVía públicaCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264852023-11-30 23:00:00-05:001.0NaNNaNHombre29NaNCaminataCosquilleoNaNNoNaNSIN DATOSIN DATOVía públicaPobladoNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264862023-11-30 23:00:00-05:001.0NaNNaNHombre40NaNCaminataDescuidoNaNNoLa Candelaria#101910Bar o cantinaCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264872023-11-30 23:20:00-05:001.0NaNNaNHombre29NaNCaminataCosquilleoNaNNoNaNSIN DATOSIN DATOVía públicaCandelariaNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00
3264882023-11-30 23:30:00-05:001.0NaNNaNMujer26NaNCaminataCosquilleoNaNNoCerro Nutibara#162116TurísticoManriqueNaNNaNNaN-1NaN2024-10-06T04:07:03.000-05:00

Duplicate rows

Most frequently occurring

fecha_hechocantidadlatitudlongitudsexoedadestado_civilmedio_transportemodalidadconducta_especialarma_medionombre_barriocodigo_barriolugarsede_receptorabiencategoria_biengrupo_bienmodelocolorfecha_ingestion# duplicates
53412021-06-15 09:00:00-05:001.06.216412-75.600636Hombre26Unión marital de hechoCaminataDescuidoNaNNoLa Loma de los Bernal#1611Vía públicaBelénHerrajesHerramientasMercancía-1NaN2024-10-06T04:07:03.000-05:0037
56002021-08-27 15:00:00-05:001.06.273619-75.550430Hombre30Soltero(a)CaminataAtracoNaNArma de fuegoCampo Valdés No.2#0303Vía públicaManriqueSin dato herramientasHerramientasMercancía-1NaN2024-10-06T04:07:03.000-05:0014
60642022-01-20 09:30:00-05:001.06.303941-75.572230Hombre28Soltero(a)AutomóvilAtracoNaNArma de fuegoPedregal#0604PanaderíaDoce de OctubreCigarrilloArtículos de fumadorMercancía-1NaN2024-10-06T04:07:03.000-05:0014
52432021-05-13 19:00:00-05:001.06.261813-75.592739Hombre40Soltero(a)CaminataAtracoNaNArma cortopunzanteBatallón Cuarta BrigadaInst_13Vía públicaLaurelesSin dato tecnologíaTecnologíaMercancía-1NaN2024-10-06T04:07:03.000-05:0011
56942021-09-23 01:00:00-05:001.06.293476-75.548202Hombre32Soltero(a)CaminataDescuidoNaNNoGranizal#0104Vía públicaPopularHerrajesHerramientasMercancía-1NaN2024-10-06T04:07:03.000-05:009
10672018-02-14 13:30:00-05:001.06.246816-75.566434Mujer38Soltero(a)CaminataCosquilleoNaNNoBarrio Colón#1013Centro comercialCandelariaTarjeta bancariaDinero, joyas, piedras preciosas y título valorMercancía-1NaN2024-10-06T04:07:03.000-05:008
46632020-10-28 02:00:00-05:001.06.251434-75.567087Hombre25Soltero(a)CaminataDescuidoNaNNoLa Candelaria#1019Vía públicaCandelariaSin dato herramientasHerramientasMercancía-1NaN2024-10-06T04:07:03.000-05:008
51702021-04-13 12:00:00-05:001.06.293769-75.564371Hombre25Unión marital de hechoCaminataDescuidoNaNNoTricentenario#0510Bus de servicio públicoCastillaElementos computadorTecnologíaMercancía-1NaN2024-10-06T04:07:03.000-05:008
53152021-06-05 11:00:00-05:001.06.245046-75.560794Hombre35Casado(a)CaminataDescuidoNaNNoBoston#1016Casa o apartamentoCandelariaSin dato herramientasHerramientasMercancía-1NaN2024-10-06T04:07:03.000-05:008
55782021-08-22 00:30:00-05:001.06.247027-75.566969Mujer60Soltero(a)CaminataCosquilleoNaNNoBarrio Colón#1013SupermercadoCandelariaTarjeta bancariaDinero, joyas, piedras preciosas y título valorMercancía-1NaN2024-10-06T04:07:03.000-05:008